AI-Based Optimization of Handover Strategy in Non-Terrestrial Networks

doi:10.12142/ZTECOM.202104011

ZTE Communications ›› 2021, Vol. 19 ›› Issue (4): 98-104.DOI: 10.12142/ZTECOM.202104011

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AI-Based Optimization of Handover Strategy in Non-Terrestrial Networks

ZHANG Chenchen(), ZHANG Nan, CAO Wei, TIAN Kaibo, YANG Zhen

State Key Laboratory of Mobile Network and Mobile Multimedia Technology, ZTE Corporation, Shenzhen 518055, China

Online:2021-12-25 Published:2022-01-04
About author:ZHANG Chenchen (zhang.cc@zte.com.cn) received his B.S. degree in mathematics from Nankai University, China in 2013, and the Ph.D. degree in computer science and technology from Shanghai Jiao Tong University, China in 2018. He has been with ZTE Corporation since 2018. He is now a senior pre-research engineer in non-terrestrial network. His main research interests include satellite communications, random access, mobility management, neural network and NOMA.|ZHANG Nan received his bachelor’s degree in communication engineering and master’s degree in integrated circuit engineering from Tongji University, China in July 2012 and March 2015, respectively. He is now a senior engineer at the Department of Algorithms, ZTE Corporation and works on the standardization of LTE and NR system. His current research interests are in the field of 5G channel modeling, MIMO, NOMA techniques, satellite/ATG communication and network architecture.|CAO Wei is a senior pre-research expert in ZTE Corporation. She received her Ph.D. degree in wireless communication from National University of Singapore in 2008. Her current research interests include non-terrestrial communication network and reconfigurable intelligent surface.|TIAN Kaibo received his master’s degree from Xi’an Jiaotong University, China in 2008. Now he is the senior pre-research expert of ZTE Corporation and responsible for the pre-research of the Air-Space-Ground integrated network technology.|YANG Zhen received his B.S. degree in communication and information system from University of Electronic Science and Technology of China in 2012. Since 2012 he has been with ZTE Corporation. He is now a senior pre-research engineer in wireless communications. His main research interests include satellite communications, random access, mobility management, neural network and DPD.

Abstract

Abstract:

Complicated radio resource management, e.g., handover condition, will trouble the user in non-terrestrial networks due to the impact of high mobility and hierarchical layouts which co-exist with terrestrial networks or various platforms at different altitudes. It is necessary to optimize the handover strategy to reduce the signaling overhead and improve the service continuity. In this paper, a new handover strategy is proposed based on the convolutional neural network. Firstly, the handover process is modeled as a directed graph. Suppose a user knows its future signal strength, then he/she can search for the best handover strategy based on the graph. Secondly, a convolutional neural network is used to extract the underlying regularity of the best handover strategies of different users, based on which any user can make near-optimal handover decisions according to its historical signal strength. Numerical simulation shows that the proposed handover strategy can efficiently reduce the handover number while ensuring the signal strength.

Key words: convolutional neural network, directed graph, handover, low earth orbit, non-terrestrial network

ZHANG Chenchen, ZHANG Nan, CAO Wei, TIAN Kaibo, YANG Zhen. AI-Based Optimization of Handover Strategy in Non-Terrestrial Networks[J]. ZTE Communications, 2021, 19(4): 98-104.

Figures/Tables 10

Figure 1 Illustration of system model

Figure 2 An example of UE near beam edge

Figure 3 Directed graph in referenced model

Figure 4 Directed graph in the proposed model

Figure 5 CNN structure for handover optimization

Table 1 Simulation parameters

Parameter	Value
Orbit altitude	600 km
Simulation scenario	Rural
Carrier frequency	2 GHz
Antenna type	Bessel antenna
Antenna aperture	2 m
EIRP	34 dBW/MHz

Figure 6 Cumulative distribution function of handover number for different w2

Figure 7 Average RSRP during simulation for different w2

Figure 8 Cumulative distribution function of handover number for different handover optimization methods

Figure 9 Average RSRP during simulation for different handover optimization methods

References 13

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AI-Based Optimization of Handover Strategy in Non-Terrestrial Networks

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